We have seen how to calculate several measures of central tendency (like mean, mode and median) in Python, using the native lists.
Now, a more memory-efficient and fast handling than lists would be to use the array object, which gives me the opportunity to introduce one of the key Python package for data science: NumPy.
What is NumPy?
NumPy, short for Numerical Python, is a module that provides high-performance (thanks to its implementation in C and Fortran) vector, matrix and higher-dimensional data structures for Python.
The array object class is the foundation of NumPy, and they are basically like lists in Python, except that have a fixed size at creation, are statically typed and homogeneous (everything inside them must be of the same type); therefore the type of the elements is determined when the array is created and this improves the performance.
NumPy arrays are also a much more efficient way of storing and manipulating data than the built-in Python lists, allowing to exchange data between different programs and systems (for example between a Python program and another C++ program).
To create vector and matrix arrays there are several methods, from Python lists or from scratch:
Continue reading “Introduction to Python package NumPy”